We produce tiles by random cropping of original image without any padding at train time (for example, by using RandomCrop transform of PyTorch [1]} in the DataLoader with desired tile size and padding = 0 as the arguments). Here we take the opportunity to note that our setting of training a CNN model with just the tiles is completely different from common practice of deep learning practitioners using random cropping in their pre-processing pipeline. In the common practice, random cropping is explicitly used as a regularization trick while taking care in selecting padding size and crop size such that the cropped image would still be recognized by a human as belonging to its label class. But in our experiments, we deliberately train with a full range of crop sizes without padding and in the case of very small sizes, the tiles could not be attributed to any class by a human, for example, as shown in Figure REF . We also clarify here that in regards to the labels of tiles, we use the same label as the parent image from which a tile is drawn, even for small tiles that could not be attributed to be of the parent image's class label. In other words, no label transformation is applied when training with tiles.
